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An immunological mechanism of resistance to CDK4/6 inhibitors in breast cancer

GSE296805 Homo sapiens (human) RNA-seq (gene expression) 50 samples Submitted 2025/05/09 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 50 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
50 samples: 50 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Author Correction: IL-17A-secreting γδ T cells promote resistance to CDK4/CDK6 inhibitors in HR(+)HER2(-) breast cancer via CX3CR1(+) macrophagesNature cancer · 2025
Summary (written by the submitters)
Resistance to CDK4/6 inhibitors leads to treatment failure and disease progression in women with HR+HER2- breast cancer (BC). We delineated a hypoxia-sensitive, CCL2-dependent pathway recruiting IL17A-secreting γδ T cells to mouse HR+HER2- BCs upon CDK4/6 inhibition, resulting in repolarization of tumor-associated macrophages (TAMs) towards an immunosuppressive CX3CR1+ phenotype associated with resistance.
Read the full summary ↓
Increased IL17A signaling and intratumoral γδ T cell abundance positively correlated with advanced grade and/or reduced survival in two cohorts of HR+HER2- BC patients. Circulating γδ T cells and plasma CCL2 levels negatively correlated with progression in an independent series of patients with HR+HER2- BC receiving CDK4/6 inhibitors. Intratumoral γδ T cells were increased in post- vs pre-treatment biopsies from HR+HER2- BC patients relapsing on CDK4/6 inhibitors. CX3CR1+ TAMs had negative prognostic impact in women with HR+HER2- BC receiving neoadjuvant PD-1 blockage and radiotherapy. Thus, γδ T cells and CX3XR1+ TAMs may favor resistance to CDK4/6 inhibitors in HR+HER2- BC patients.
Published in
Author Correction: IL-17A-secreting γδ T cells promote resistance to CDK4/CDK6 inhibitors in HR(+)HER2(-) breast cancer via CX3CR1(+) macrophages
Petroni G, Galassi C, Gouin KH 3rd et al. · Nature cancer 2025 · PMID 40702362 · doi:10.1038/s43018-025-01036-8
This dataset
Open GSE296805 in finder → Find more human RNA-seq datasets → Breast cancer landscape →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE296805_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1261072. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40702362) ↗
Study design
50 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 50 sample titles: 50 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 10 more — browse all 50 samples with per-sample file links →

More Breast cancer datasets

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.